Cognitive Issues & User Tasks

Transcription

1 Cognitive Issues & User Tasks Information Visualization April 23, 2008 Carsten Görg Slides adapted from John Stasko Housekeeping First assignment due today Second assignment due next Wednesday to and cc to Summer term

2 Housekeeping Webpage: Register for class today: (Name, Matrikelnummer, Studiengang, Semesterzahl) Summer term Outline Overview 1. Role How visualizations aid cognition? 2. Tasks What does the visualization assist? Summer term

3 Basic Premise Understanding (the cognitive aspects) is the crucial part of InfoVis Visualization is simply a tool useful for aiding comprehension and understanding Discussed the role of external cognition aids briefly earlier in term Summer term How Are Graphics Used? What does a visualization or graphic image provide for us? Summer term

4 How Are Graphics Used? Larkin & Simon 87 investigated usefulness of graphical displays Graphical visualization could support more efficient task performance by: Allowing substitution of rapid perceptual influences for difficult logical inferences Reducing search for information required for task completion (Sometimes text is better, however) Summer term Understanding People utilize an internal model that is generated based on what is observed B. Tversky calls the internal model a cognitive map Think about that term Summer term

5 Example You re taking the Saarbahn to get to the train station You have some existing internal model of the system, stops, how to get there On train, you glance at the map for help Refines your internal model, clarifying items and extending it Note that it s still not perfect, no internal model ever is Summer term Cognitive Map Just don t have one big one Have large number of these for all different kinds of things Collection of cognitive maps --> Cognitive collage Summer term

6 1. Process Models Process by which a person looks at a graphic and makes some use of it A number of substeps probably exist Can you describe process? Summer term Process Model 1 Robert Spence Navigation - Creation and interpretation of an internal mental model Summer term

7 Navigation Content Browse Model Browsing strategy Formulate a browsing strategy Interpretation Interpret Internal model Summer term Interpretation - Content is the display on screen - Modeling of that pattern results in cognitive map - Interpretation (variables x and y are related) leads to new view, that generates an idea for a new browsing strategy - Look at the display again with that Summer term

8 Process Model 2 Card, Mackinlay, Shneiderman book Knowledge crystallization task Gather info for some purpose, make sense of it by constructing a representational framework, and package it into a form for communication or action Summer term Knowledge Crystallization Information foraging Search for schema (representation) Instantiate schema Problem solve to trade off features Search for a new schema that reduces problem to a simple trade-off Package the patterns found in some output product From CMS 98 Summer term

9 How Vis Amplifies Cognition Increasing memory and processing resources available Reducing search for information Enhancing the recognition of patterns Enabling perceptual inference operations Using perceptual attention mechanisms for monitoring Encoding info in a manipulable medium Summer term Process task Raw data Data tables Visual Structures Views Data transformations Visual mappings View transformations Summer term

10 Knowledge Crystallization Overview Zoom Filter Details-on-demand Browse Search query Forage for data Task Author, decide or act Extract Compose Reorder Cluster Class Average Promote Detect pattern Abstract Search for schema Instantiate schema Instantiate Problem-solve Read fact Read comparison Read pattern Manipulate Create Delete Summer term User Tasks What things will people want to accomplish using information visualizations? Earlier, we briefly discussed search vs. browsing Summer term

11 Browsing vs. Search Important difference in activities Appears that information visualization may have more to offer to browsing But browsing is a softer, fuzzier activity So, how do we articulate utility? Maybe describe when it s useful When is browsing useful? Summer term Browsing Useful when Good underlying structure so that items close to one another can be inferred to be similar Users are unfamiliar with collection contents Users have limited understanding of how system is organized and prefer less cognitively loaded method of exploration Users have difficulty verbalizing underlying information need Information is easier to recognize than describe Lin 97 Summer term

12 Thought Maybe infovis isn t about answering questions or solving problems hmmm Maybe it s about asking better questions Summer term Tasks OK, but browsing and search are very high level Let s be more specific Summer term

13 Example from Earlier Questions: Which state has the highest income? Is there a relationship between income and education? Are there any outliers? Summer term 2008 Example courtesy 28 of Chris North Exercise What are the (types of) tasks being done here? Can you think of others? Let s develop a list Summer term

14 Task Taxonomies Number of different ones exist, important to understand what process they focus on Creating an artifact Human tasks Tasks using visualization system... Summer term User Tasks Wehrend & Lewis created a low-level, domain independent taxonomy of user tasks in visualization environments Eleven basic actions identify, locate, distinguish, categorize, cluster, distribution, rank, compare within relations, compare between relations, associate, correlate Vis 90 Summer term

15 Another Taxonomy Amar, Eagan, Stasko, InfoVis 05 Summer term Background Use commercial tools class assignment Students generate questions to be answered using commercial infovis systems Data sets: Domain Data cases Attributes Questions Generated Cereals Mutual funds Cars Films Grocery surveys Generated 596 total analysis tasks Summer term

16 Summer term Summer term

17 Summer term Terminology Data case An entity in the data set Attribute A value measured for all data cases Aggregation function A function that creates a numeric representation for a set of data cases (eg, average, count, sum) Summer term

18 1. Retrieve Value General Description: Given a set of specific cases, find attributes of those cases. Examples: - What is the mileage per gallon of the Audi TT? - How long is the movie Gone with the Wind? Summer term Filter General Description: Given some concrete conditions on attribute values, find data cases satisfying those conditions. Examples: - What Kellogg's cereals have high fiber? - What comedies have won awards? - Which funds underperformed the SP-500? Summer term

19 3. Compute Derived Value General Description: Given a set of data cases, compute an aggregate numeric representation of those data cases. Examples: - What is the gross income of all stores combined? - How many manufacturers of cars are there? - What is the average calorie content of Post cereals? Summer term Find Extremum General Description: Find data cases possessing an extreme value of an attribute over its range within the data set. Examples: - What is the car with the highest MPG? - What director/film has won the most awards? - What Robin Williams film has the most recent release date? Summer term

20 5. Sort General Description: Given a set of data cases, rank them according to some ordinal metric. Examples: - Order the cars by weight. - Rank the cereals by calories. Summer term Determine Range General Description: Given a set of data cases and an attribute of interest, find the span of values within the set. Examples: - What is the range of film lengths? - What is the range of car horsepowers? - What actresses are in the data set? Summer term

21 7. Characterize Distribution General Description: Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute s values over the set. Examples: - What is the distribution of carbohydrates in cereals? - What is the age distribution of shoppers? Summer term Find Anomalies General Description: Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers. Examples: - Are there any outliers in protein? - Are there exceptions to the relationship between horsepower and acceleration? Summer term

22 9. Cluster General Description: Given a set of data cases, find clusters of similar attribute values. Examples: - Are there groups of cereals w/ similar fat/calories/sugar? - Is there a cluster of typical film lengths? Summer term Correlate General Description: Given a set of data cases and two attributes, determine useful relationships between the values of those attributes. Examples: - Is there a correlation between carbohydrates and fat? - Is there a correlation between country of origin and MPG? - Do different genders have a preferred payment method? - Is there a trend of increasing film length over the years? Summer term

23 Discussion/Reflection Compound tasks Sort the cereal manufacturers by average fat content Compute derived value; Sort Which actors have co-starred with Julia Roberts? Filter; Retrieve value Summer term Discussion/Reflection What questions were left out? Basic math Which cereal has more sugar, Cheerios or Special K? Compare the average MPG of American and Japanese cars. Uncertain criteria Does cereal (X, Y, Z ) sound tasty? What are the characteristics of the most valued customers? Higher-level tasks How do mutual funds get rated? Are there car aspects that Toyota has concentrated on? More qualitative comparison How does the Toyota RAV4 compare to the Honda CRV? What other cereals are most similar to Trix? Summer term

24 Concerns InfoVis tools may have influenced students questions Graduate students as group being studied How about professional analysts? Subjective Not an exact science Summer term Contributions Set of grounded low-level analysis tasks Potential use of tasks as a language/vocabulary for comparing and evaluating infovis systems Summer term

25 Paper Recap The Eyes Have It: A Task by Data Type Taxonomy for Information Visualization Ben Shneiderman Visual Languages 1996 Summer term Shneiderman s Mantra 1. Overview 2. Zoom and Filter 3. Details on Demand Summer term

26 Overview Data Types 1. 1D 2. 2D 3. 3D 4. Temporal 5. ND 6. Tree 7. Network Tasks 1. Overview 2. Zoom 3. Filter 4. Details-on-demand 5. Relate 6. History 7. Extract Summer term Paper Recap Inventing discovery tools: combining Information visualization with data mining Ben Shneiderman Information Visualization 02 Summer term

27 Introduction Two lines of research Information Visualization Data Mining Issues influencing the design of Discovery tools: Statistical Algorithms vs. Visual data presentation Hypothesis testing vs. exploratory data analysis Summer term Statistical algorithms vs. visual data presentation Statistical Algorithms Pros Cons Visual Presentations Pros Cons Summer term

28 Conclusion and Recommendations Integrate data mining and information visualization Allow users to specify what they are seeking Recognize that users are situated in a social context Respect human responsibility Summer term Visual Analytics Formation of abstract visual metaphors in combination with human information discourse (interaction) that enables detection of the expected and discovery of the unexpected within massive, dynamically changing information spaces Intelligence analysis Bioinformatics Financial analysis Wong & Thomas IEEE CG&A, 04 Summer term

29 References Spence & CMS texts All referred to papers Summer term